{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "9b2cba1c",
   "metadata": {},
   "source": [
    "# Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "4b0b1683",
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import os\n",
    "import numpy as np\n",
    "\n",
    "import json\n",
    "\n",
    "from transformers import MarianTokenizer, AutoTokenizer\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import collections\n",
    "from collections import defaultdict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "5b1607e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "src_lang = \"en\"\n",
    "translator = \"mbart50\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "adf37372",
   "metadata": {},
   "source": [
    "# Defining plot styles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "603516f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.stats import pearsonr, kde, gaussian_kde\n",
    "import matplotlib\n",
    "from matplotlib import rc\n",
    "\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "6c6d06be",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "plt.rcParams['font.family'] = 'serif'\n",
    "plt.rcParams['text.usetex'] = True\n",
    "plt.rcParams['text.usetex'] = False\n",
    "plt.rcParams['font.family'] = 'serif'\n",
    "plt.rcParams['font.serif'] = ['Times New Roman']\n",
    "\n",
    "plt.rcParams['font.monospace'] = 'Ubuntu Mono'\n",
    "plt.rcParams['font.size'] = 26\n",
    "plt.rcParams['axes.labelsize'] = 26\n",
    "plt.rcParams[\"axes.labelweight\"] = \"bold\"\n",
    "plt.rcParams['axes.titlesize'] = 26\n",
    "plt.rcParams['axes.titleweight'] = 'bold'\n",
    "plt.rcParams['xtick.labelsize'] = 16\n",
    "plt.rcParams['ytick.labelsize'] = 16\n",
    "plt.rcParams['legend.fontsize'] = 18\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc0404cb",
   "metadata": {},
   "source": [
    "# Load / Process Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "0750868d",
   "metadata": {},
   "outputs": [],
   "source": [
    "RECALL=-3\n",
    "PRECISION=-1\n",
    "\n",
    "TRANSLATOR=\"mbart50\"\n",
    "SRC_LANG=\"en\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "c1aafcb8",
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_old_tokens_dict(tokens_file):\n",
    "    with open(tokens_file, \"r\") as f:\n",
    "        tokens_str = (f.readlines())[0]\n",
    "    tokens_str = tokens_str.replace(\"'\", \"\\\"\")\n",
    "    tokens_dict = json.loads(tokens_str)\n",
    "    \n",
    "    if 'worker' in tokens_dict:\n",
    "        tokens_dict.pop('worker')\n",
    "        \n",
    "    return tokens_dict\n",
    "\n",
    "\n",
    "def create_tokens_dict(variants_file, translator, src_lang, tgt_lang):\n",
    "    tokens_dict = defaultdict(dict)\n",
    "    tokenizer = AutoTokenizer.from_pretrained(\"facebook/mbart-large-50-many-to-many-mmt\")\n",
    "    \n",
    "    with open(variants_file, 'r') as var_json:\n",
    "        variants_dict = json.load(var_json)\n",
    "    \n",
    "    for prof_gender, variants in variants_dict.items():\n",
    "        prof, gender = prof_gender.split('-')\n",
    "        gender = gender.capitalize()\n",
    "        for v_idx, var in enumerate(variants):\n",
    "            tokenized = tokenizer(var, add_special_tokens=False)\n",
    "            num_tokens = len(tokenized['input_ids'])\n",
    "            \n",
    "            tokens_dict[f\"{prof}-{str(v_idx)}\"][gender] = num_tokens\n",
    "    return tokens_dict\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "d538b214",
   "metadata": {},
   "outputs": [],
   "source": [
    "tokens_dict_he = create_tokens_dict(\"../data/wino_mt/he_variants.json\",TRANSLATOR, SRC_LANG, \"he\")\n",
    "tokens_dict_de = create_tokens_dict(\"../data/wino_mt/de_variants.json\",TRANSLATOR, SRC_LANG, \"de\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "441cd88c",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"../data/male_stereotype\",\"r\") as f:\n",
    "    male_stereotype = f.readlines()\n",
    "    male_stereotype = {i.strip().lower() for i in male_stereotype}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "789ffaf4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'analyst',\n",
       " 'carpenter',\n",
       " 'ceo',\n",
       " 'chief',\n",
       " 'construction worker',\n",
       " 'cook',\n",
       " 'developer',\n",
       " 'driver',\n",
       " 'farmer',\n",
       " 'guard',\n",
       " 'janitor',\n",
       " 'laborer',\n",
       " 'lawyer',\n",
       " 'manager',\n",
       " 'mechanic',\n",
       " 'mover',\n",
       " 'physician',\n",
       " 'salesperson',\n",
       " 'sheriff',\n",
       " 'supervisor'}"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "male_stereotype"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0bf6d406",
   "metadata": {},
   "source": [
    "# Create Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "bb89a3fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "def graph_5_delta_g(results_file,tokens_dict,lang):\n",
    "    with open(results_file, \"r\") as f:\n",
    "        lines = f.readlines()\n",
    "        recalls_str = lines[RECALL]\n",
    "        recalls_str = recalls_str.replace(\"'\", \"\\\"\")\n",
    "        recalls_dict = json.loads(recalls_str)\n",
    "\n",
    "        precisions_str = lines[PRECISION]\n",
    "        precisions_str = precisions_str.replace(\"'\", \"\\\"\")\n",
    "        precisions_dict = json.loads(precisions_str)\n",
    "\n",
    "    professions = list(tokens_dict.keys())\n",
    "    delta_t_dict = dict()\n",
    "\n",
    "    \n",
    "    for p in professions:\n",
    "        delta_t_dict[p] = tokens_dict[p]['Male'] - tokens_dict[p]['Female']\n",
    "    delta_t_dict = dict(sorted(delta_t_dict.items(), key=lambda item: item[1]))\n",
    "\n",
    "\n",
    "    x = []\n",
    "    y = []\n",
    "    \n",
    "    data = pd.DataFrame({\"Delta T\": [], \"Delta G\": [], \"M F1\": [], \"F F1\": [], \"M T\": [], \"F T\": []})\n",
    "\n",
    "    for prof, delta_t in delta_t_dict.items():\n",
    "        prof = prof.lower()\n",
    "        if prof in recalls_dict and prof in precisions_dict:\n",
    "            r_m,r_f=recalls_dict[prof]['male_recall'],recalls_dict[prof]['female_recall']\n",
    "            p_m,p_f=precisions_dict[prof]['male_precision'],precisions_dict[prof]['female_precision']\n",
    "            if r_m ==0 and p_m == 0:\n",
    "                male_f1 = 0\n",
    "            else:\n",
    "                male_f1= 2 * (r_m*p_m)/(r_m+p_m)\n",
    "    \n",
    "            if r_f ==0 and p_f == 0:\n",
    "                female_f1 = 0\n",
    "            else:\n",
    "                female_f1= 2 * (r_f*p_f)/(r_f+p_f)\n",
    "                \n",
    "            data = data.append({\"Delta T\": delta_t, \"Delta G\": male_f1 - female_f1,\n",
    "                                \"M F1\": male_f1, \"F F1\": female_f1,\n",
    "                                \"M T\": tokens_dict[prof]['Male'], \"F T\": tokens_dict[prof]['Female']},\n",
    "                               ignore_index=True)\n",
    "\n",
    "\n",
    "    f, ax = plt.subplots(figsize=(7, 6))\n",
    "\n",
    "    sns.boxplot(\n",
    "        data=data, x=\"Delta T\", y=\"Delta G\",\n",
    "        orient=\"v\",\n",
    "        notch=False, showcaps=False, showmeans=False,\n",
    "        boxprops={\"facecolor\": (.4, .6, .8, .5)},\n",
    "        medianprops={\"color\": \"coral\"})\n",
    "    sns.stripplot(x=\"Delta T\", y=\"Delta G\", data=data,\n",
    "              size=4, color=\".3\", linewidth=0)\n",
    "    \n",
    "#     z = np.polyfit(data['Delta T'], data['Delta G'], 1)\n",
    "#     p = np.poly1d(z)\n",
    "#     ax.plot(data['Delta T'] + len(data['Delta T'].unique())- 1, p(data['Delta T']), \"r--\")\n",
    "\n",
    "    # Tweak the visual presentation\n",
    "    ax.yaxis.grid(True)\n",
    "    sns.despine(trim=False)\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    plt.savefig(f'../graphs/graph_5_{lang}_delta_g_mbart.pdf')\n",
    "    plt.show()\n",
    "    \n",
    "    print(\"correlation\")\n",
    "    print(data.corr())\n",
    "    \n",
    "    tidy = data.melt(value_vars=['M F1','F F1'], id_vars=['M T'], value_name='F1',var_name='gender')\n",
    "    print(tidy)\n",
    "    \n",
    "    sns.catplot(x=\"M T\", y='F1',\n",
    "        data=tidy, kind=\"bar\", hue='gender', \n",
    "        palette=sns.color_palette(['lightblue', 'brown']),\n",
    "        height=4.5, aspect=1.8)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "4fdcd6c0",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_15240/2078070108.py:41: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta G\": male_f1 - female_f1,\n",
      "/tmp/ipykernel_15240/2078070108.py:41: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta G\": male_f1 - female_f1,\n",
      "/tmp/ipykernel_15240/2078070108.py:41: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta G\": male_f1 - female_f1,\n",
      "/tmp/ipykernel_15240/2078070108.py:41: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta G\": male_f1 - female_f1,\n",
      "/tmp/ipykernel_15240/2078070108.py:41: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta G\": male_f1 - female_f1,\n",
      "/tmp/ipykernel_15240/2078070108.py:41: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta G\": male_f1 - female_f1,\n",
      "/tmp/ipykernel_15240/2078070108.py:41: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta G\": male_f1 - female_f1,\n",
      "/tmp/ipykernel_15240/2078070108.py:41: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta G\": male_f1 - female_f1,\n",
      "/tmp/ipykernel_15240/2078070108.py:41: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta G\": male_f1 - female_f1,\n",
      "/tmp/ipykernel_15240/2078070108.py:41: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta G\": male_f1 - female_f1,\n",
      "/tmp/ipykernel_15240/2078070108.py:41: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta G\": male_f1 - female_f1,\n",
      "/tmp/ipykernel_15240/2078070108.py:41: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta G\": male_f1 - female_f1,\n",
      "/tmp/ipykernel_15240/2078070108.py:41: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta G\": male_f1 - female_f1,\n",
      "/tmp/ipykernel_15240/2078070108.py:41: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta G\": male_f1 - female_f1,\n",
      "/tmp/ipykernel_15240/2078070108.py:41: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta G\": male_f1 - female_f1,\n",
      "/tmp/ipykernel_15240/2078070108.py:41: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta G\": male_f1 - female_f1,\n",
      "/tmp/ipykernel_15240/2078070108.py:41: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta G\": male_f1 - female_f1,\n",
      "/tmp/ipykernel_15240/2078070108.py:41: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta G\": male_f1 - female_f1,\n",
      "/tmp/ipykernel_15240/2078070108.py:41: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta G\": male_f1 - female_f1,\n",
      "/tmp/ipykernel_15240/2078070108.py:41: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta G\": male_f1 - female_f1,\n",
      "/tmp/ipykernel_15240/2078070108.py:41: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta G\": male_f1 - female_f1,\n",
      "/tmp/ipykernel_15240/2078070108.py:41: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta G\": male_f1 - female_f1,\n",
      "/tmp/ipykernel_15240/2078070108.py:41: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta G\": male_f1 - female_f1,\n",
      "/tmp/ipykernel_15240/2078070108.py:41: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta G\": male_f1 - female_f1,\n",
      "/tmp/ipykernel_15240/2078070108.py:41: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta G\": male_f1 - female_f1,\n",
      "/tmp/ipykernel_15240/2078070108.py:41: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta G\": male_f1 - female_f1,\n",
      "/tmp/ipykernel_15240/2078070108.py:41: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta G\": male_f1 - female_f1,\n",
      "/tmp/ipykernel_15240/2078070108.py:41: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta G\": male_f1 - female_f1,\n",
      "/tmp/ipykernel_15240/2078070108.py:41: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta G\": male_f1 - female_f1,\n",
      "/tmp/ipykernel_15240/2078070108.py:41: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta G\": male_f1 - female_f1,\n",
      "/tmp/ipykernel_15240/2078070108.py:41: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta G\": male_f1 - female_f1,\n",
      "/tmp/ipykernel_15240/2078070108.py:41: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta G\": male_f1 - female_f1,\n",
      "/tmp/ipykernel_15240/2078070108.py:41: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta G\": male_f1 - female_f1,\n",
      "/tmp/ipykernel_15240/2078070108.py:41: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta G\": male_f1 - female_f1,\n",
      "/tmp/ipykernel_15240/2078070108.py:41: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta G\": male_f1 - female_f1,\n",
      "/tmp/ipykernel_15240/2078070108.py:41: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta G\": male_f1 - female_f1,\n",
      "/tmp/ipykernel_15240/2078070108.py:41: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta G\": male_f1 - female_f1,\n",
      "/tmp/ipykernel_15240/2078070108.py:41: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta G\": male_f1 - female_f1,\n",
      "/tmp/ipykernel_15240/2078070108.py:41: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta G\": male_f1 - female_f1,\n",
      "/tmp/ipykernel_15240/2078070108.py:41: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta G\": male_f1 - female_f1,\n",
      "/tmp/ipykernel_15240/2078070108.py:41: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta G\": male_f1 - female_f1,\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 700x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "correlation\n",
      "          Delta T   Delta G      M F1      F F1       M T       F T\n",
      "Delta T  1.000000 -0.502313 -0.277014  0.381269  0.527246 -0.125849\n",
      "Delta G -0.502313  1.000000  0.568096 -0.745568 -0.246695  0.084402\n",
      "M F1    -0.277014  0.568096  1.000000  0.124892 -0.420465 -0.285515\n",
      "F F1     0.381269 -0.745568  0.124892  1.000000 -0.043071 -0.332965\n",
      "M T      0.527246 -0.246695 -0.420465 -0.043071  1.000000  0.776604\n",
      "F T     -0.125849  0.084402 -0.285515 -0.332965  0.776604  1.000000\n",
      "    M T gender        F1\n",
      "0   1.0   M F1  0.735294\n",
      "1   1.0   M F1  0.677419\n",
      "2   1.0   M F1  0.821053\n",
      "3   1.0   M F1  0.829787\n",
      "4   1.0   M F1  0.808081\n",
      "..  ...    ...       ...\n",
      "77  3.0   F F1  0.411765\n",
      "78  2.0   F F1  1.000000\n",
      "79  2.0   F F1  0.941176\n",
      "80  3.0   F F1  1.000000\n",
      "81  2.0   F F1  1.000000\n",
      "\n",
      "[82 rows x 3 columns]\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA4kAAAGICAYAAADs7aXZAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAA9hAAAPYQGoP6dpAAA/D0lEQVR4nO3de1iUdf7/8dcgiMpJFJVWUcxjHnI1bV3RMqk1sTUrZZVKKTtaibp981B9syxPm1sonjIt+6YrmUmZlAqVipiK0lEjLUkwRTDlqBxkfn/4486J0wADA7PPx3XNdc3c9+d+3++Z7rKX931/bpPZbDYLAAAAAABJTvZuAAAAAABQfxASAQAAAAAGQiIAAAAAwEBIBAAAAAAYCIkAAAAAAAMhEQAAAABgICQCAAAAAAyERAAAAACAgZBoJ2azWVlZWTKbzfZuBQAAAAAMhEQ7yc7OlpeXl7Kzs+3dCgAAAAAYCIkAAAAAAAMhEQAAAABgICQCAAAAAAyERAAAAACAgZAIAAAAADA4XEhMSkrS0qVLFRoaqt69e8vZ2Vkmk0kvv/xyjerGxMQoKChIPj4+atq0qbp3765nn31WOTk5NuocAAAAAOzP2d4N2NqKFSsUHh5u05qvvfaapk+fLpPJpCFDhqhNmzbas2eP5s2bp82bNysuLk4+Pj423ScAAAAA2IPDnUns1auXnn76aa1fv15Hjx7V/fffX6N6iYmJ+uc//6lGjRpp27Zt2rVrl9577z399NNPCgwMVFJSkh577DEbdQ8AAAAA9uVwZxIfeughi89OTjXLwfPnz5fZbNYDDzygESNGGMubNWumNWvW6Nprr9XmzZv1ww8/qHv37jXaFwAAAADYm8OdSbSlgoICbdu2TZIUEhJSan2HDh0UEBAgSdqyZUud9gYAAAAAtYGQWIEff/xReXl5kqT+/fuXOaZkeWJiYp31BQAAAAC1xeEuN7WlEydOSJKaN28uDw+PMsf4+flZjAUAAID9hYWFKT09XZLUqlUrm09sCDgyQmIFsrOzJUlubm7ljnF3d5ckZWVlVVgrPz9f+fn5xufKxgMAAKD60tPTlZaWZu82gAaJy03ryPz58+Xl5WW8Ss5AAgAAAEB9QkisQMklprm5ueWOycnJkSR5enpWWGvWrFnKzMw0XikpKbZrFAAAAABshMtNK+Dv7y9JunDhgrKzs8u8L7Ek7JWMLY+rq6tcXV1t3SIAAAAA2BRnEivQrVs3NWvWTJKUkJBQ5piS5f369auzvgAAAACgthASK9C4cWONHDlSkrRhw4ZS63/55RfFx8dLku6666467Q0AAAAAagMhUVJERIS6d++uCRMmlFo3c+ZMmUwmvfXWW/r000+N5Xl5eZo0aZIuX76se+65R927d6/LlgEAAACgVjjcPYmHDx/W5MmTjc8//fSTJGnVqlX6+OOPjeVbtmzRNddcI0nKyMhQUlKSfH19S9Xr16+fFi9erOnTpysoKEg333yzWrdurT179uj06dPq1q2bVq5cWcvfCgAAAADqhsOFxKysLO3fv7/U8tTUVKWmphqfr35mYWWmTZum3r17a/HixTpw4IByc3PVvn17zZo1S7NmzSpzQhsAAAAAaIhMZrPZbO8m/htlZWXJy8tLmZmZlT4+AwAAAFUTEhKitLQ0SVKbNm3KnF8CQNm4JxEAAAAAYCAkAgAAAAAMhEQAAAAAgIGQCAAAAAAwEBIBAAAAAAZCIgAAAADAQEgEAAAAABgIiQAAAAAAAyERAAAAAGAgJAIAAAAADM72bgAAaiIsLEzp6emSpFatWik8PNzOHQEAADRshEQADVp6errS0tLs3QYAAIDD4HJTAAAAAICBkAgAAAAAMBASAQAAAAAG7kkEAAfABD4AAMBWCIkA4ACYwAcAANgKl5sCAAAAAAyERAAAAACAgZAIAAAAADAQEgEAAAAABkIiAAAAAMBASAQAAAAAGAiJAAAAAAADz0lEtfDgbgAAAMAxERJRLTy4GwAAAHBMXG4KAAAAADBwJhGATWz7Ptku+71YWGTx3l59SNLInv522zcAAICtcCYRAAAAAGAgJAIAAAAADIREAAAAAICBkAgAAAAAMBASAQAAAAAGQiIAAAAAwEBIBAAAAAAYCIkAAAAAAAMhEQAAAABgICQCAAAAAAyERAAAAACAwdneDQAA4CjCwsKUnp4uSWrVqpXCw8Pt3BEAAFVHSAQAwEbS09OVlpZm7zYAAKgRhw2JmzZt0rJly/T111+roKBAnTt31r333qtp06bJxcWlSrVyc3O1ZMkSbd68WT/++KMuXryoli1bqn///nrkkUc0atSoWvoWFdv2fbJd9itJFwuLLN7bq5eRPf3tsl8AAADAUTlkSJw6darCw8Pl7OysYcOGyd3dXZ999plmzJihrVu3aseOHWratKlVtc6dO6ebbrpJR44ckbu7uwYNGqTmzZvr+PHj2rZtm7Zt26YpU6ZwSREAAAAAh+BwE9dERUUpPDxc7u7u2r9/v7Zv367Nmzfr2LFj6t27t+Li4vT8889bXe+ll17SkSNHdMMNN+iXX37R9u3bFRkZqUOHDmnbtm1ydnbWkiVL9OWXX9bitwIAAACAuuFwIXHevHmSpJkzZ6pfv37Gch8fHy1fvlySFBERoczMTKvqffbZZ5KkGTNmqEWLFhbrgoKCdMstt0iS9u3bV+PeAVSde3NvebZoKc8WLeXe3Nve7QAAADR4DnW56alTp3Tw4EFJUkhISKn1gwcPlp+fn1JSUhQdHa3x48dXWrNJkyZW7dvHx6dqzQKwiXFTZ9m7BQAAAIfiUGcSExMTJUktWrRQx44dyxzTv39/i7GVGTFihCRp4cKF+u233yzWRUdH6/PPP5evr6/dJq8BAAAAAFtyqDOJJ06ckCS1b9++3DF+fn4WYyszY8YMHThwQNu3b1eHDh0UEBBgTFxz6NAhBQQEaM2aNfLy8qr5FwAAAAAAO3OokJidnS1JcnNzK3eMu7u7JCkrK8uqmm5ubtq6datmz56txYsXa/v27ca6li1b6tZbb1Xbtm0rrZOfn6/8/Hzjs7X7BwAAAIC65FAhsTacPn1ad955p7755hu9/PLLGj9+vFq3bq0jR47oueee04svvqioqCjt2bNHHh4e5daZP3++XnzxxTrsHEBdWzFokN32ne3qKjlduYMg+/Rpu/XyeHy8XfYLAABsx6HuSSwJabm5ueWOycnJkSR5enpaVXPixIk6ePCg5s6dq9mzZ6tjx45yc3PTgAED9PHHH6t37976+uuv9eqrr1ZYZ9asWcrMzDReKSkpVn4r1FdhYWEKCQlRSEiIwsLC7N0OAAAAYBMOFRL9/f0lqcIAVrKuZGxFTp06pZ07d0pSmTOhuri4aMyYMZKkmJiYCmu5urrK09PT4oWGLT09XWlpaUpLS1N6erq92wEAAABswqFCYt++fSVJ586dK3dimoSEBEmyeIZieU6ePGm8Ly/UlUxY88eZTwEAAACgIXKokNiuXTsNGDBAkrRhw4ZS6+Pi4pSSkiJXV1cFBQVVWu/qCWn2799f5pgvv/xSksp95AYAAAAANCQOFRIlafbs2ZKkBQsW6PDhw8byc+fOafLkyZKkJ5980uKRFVu2bFH37t0VGBhoUat9+/ZG6AwLC1NycrLF+nfffVeRkZGSpJCQEJt/FwAAGhru1waAhs/hZjcdPXq0pkyZoiVLlmjgwIEKDAyUm5ubYmNjdeHCBQUEBGju3LkW22RmZiopKUmXLl0qVW/t2rW65ZZbdPToUV133XUaOHCgfHx8dPToUX3//feSpPvuu0/33ntvnXw/AADqs5L7tQEADZfDhURJCg8PV0BAgJYtW6b4+HgVFhaqU6dOmjlzpqZNm6bGjRtbXatXr1767rvv9Nprr+mTTz7RwYMHlZ+fL29vbw0fPlwPPviggoODa/HbAAAAAEDdcciQKEnBwcFWh7fQ0FCFhoaWu75NmzZasGCBFixYYKPuAAAAAKB+ctiQiNrl3ty7zPcAAAAAGjZCIqpl3NRZ9m4BAAAAQC1wuNlNAQAAAADVR0gEAAAAABgIiQAAAAAAAyERAAAAAGAgJAIAAAAADIREAAAAAICBkAgAAAAAMBASAQAAAAAGQiIAAAAAwEBIBAAAAAAYCIkAAAAAAAMhEQAAAABgICQCAAAAAAyERAAAAACAwdneDQAAAMC2wsLClJ6eLklq1aqVwsPD7dwRgIaEkAgAAOBg0tPTlZaWZu82ADRQXG4KAAAAADAQEgEAAAAABkIiAAAAAMBASAQAAAAAGAiJAAAAAAADIREAAAAAYCAkAgAAAAAMhEQAAAAAgIGQCAAAAAAwEBIBAAAAAAZCIgAAAADAQEgEAAAAABgIiQAAAAAAAyERAAAAAGAgJAIAAAAADIREAAAAAICBkAgAAAAAMDjbuwEAQM01NZul4uLf3wMAAFQTIREAHMAtBQX2bgEAyrRi0CC77Dfb1VVyunLRXPbp03brQ5Iej4+3276B6uByUwAAAACAgZAIAAAAADAQEgEAAAAABkIiAAAAAMDgsCFx06ZNGjp0qLy9veXm5qY+ffpo0aJFKiwsrHbNDz/8UKNGjZKvr68aN26s1q1ba9CgQXrppZds2DkAAAAA2I9DhsSpU6cqODhYe/fu1Y033qjbb79dJ0+e1IwZMzRs2DBdvHixSvUKCgoUHBys0aNHKyYmRj179tSYMWPUq1cv/fTTT1qyZEktfRMAAAAAqFsO9wiMqKgohYeHy93dXbt27VK/fv0kSRkZGRo2bJji4uL0/PPP69VXX7W65sMPP6xNmzZp9OjRWr16tXx8fIx1xcXFOnDggM2/BwAAAADYg8OdSZw3b54kaebMmUZAlCQfHx8tX75ckhQREaHMzEyr6sXGxuqdd95Rr1699N5771kERElycnLSwIEDbdQ9AAAAANiXQ51JPHXqlA4ePChJCgkJKbV+8ODB8vPzU0pKiqKjozV+/PhKay5dulTSlUtYXVxcbNswasyeD8blIb1A/bTt+2S77ftiYZHFe3v1MrKnv132CwBwDA4VEhMTEyVJLVq0UMeOHcsc079/f6WkpCgxMbHSkHj58mXFxsZKkm666SadOXNGGzduVFJSklxdXdW3b1/dc889cnd3t+0XAQAAAAA7caiQeOLECUlS+/btyx3j5+dnMbYiP//8s3JyciRJX375pSZPnmx8LvE///M/2rhxo4YNG1bdtgEAAACg3nCoexKzs7MlSW5ubuWOKTnrl5WVVWm9c+fOGe8nTZqkG264QQcPHlR2dra++uorBQUFKT09XXfeeaeOHTtWYa38/HxlZWVZvAAAAACgvnGokGhrZrPZeN+2bVtt375d/fv3l7u7u/r06aOPPvpIvXr1Uk5OjhYsWFBhrfnz58vLy8t4lZzRBAAAAID6xKFCooeHhyQpNze33DEll4t6enpaXU+SQkND5erqarG+UaNGevTRRyVJMTExFdaaNWuWMjMzjVdKSkql+wcAAACAuuZQIdHf31+SKgxgJetKxlZWz2QySZKuvfbaMseULD99+nSFtVxdXeXp6WnxAgAAAFA/DB06VCaTSSaTSXPmzLF3O3ZVbyau+f7773Xo0CGLZRMmTKhSjb59+0q6ci/hiRMnypzhNCEhQZIsnqFYHnd3d3Xr1k0//PCDMjIyyhxTspwZTgEAAAA4gnpzJvHjjz/WAw88YPGqqnbt2mnAgAGSpA0bNpRaHxcXp5SUFLm6uiooKMiqmmPHjpVU/uWkO3fulCTdeOONVe4XAAAAAOqbehMSpd8nirl6wpiqmj17tiRpwYIFOnz4sLH83Llzmjx5siTpySeflJeXl7Fuy5Yt6t69uwIDA0vVmzJliry9vRUdHa1Vq1ZZrNu4caPWr19vjAMAAACAhq5ehURbGD16tKZMmaKcnBwNHDhQI0aM0JgxY9S5c2d9++23CggI0Ny5cy22yczMVFJSkn766adS9Xx8fBQZGakmTZroscceU69evTR27Fj169dP48ePl9ls1vPPP2/1mUkAAAAAqM8cLiRKUnh4uCIjI/XXv/5V8fHxio6OVrt27bRgwQJ99tlnatq0aZXq3Xbbbfr66681ceJEXbhwQR9++KFOnjypoKAgbd++XS+99FItfRMAAAAAqFv1ZuIaWwsODlZwcLBVY0NDQxUaGlrhmK5du+rtt9+ueWMAANSyFYMG2W3f2a6uktOVv4POPn3arr08Hh9vt30DQENWrZD4zjvv2LqPUjObAgAAAKi6pKQkrV69Wp9++qlSUlJUXFystm3bauDAgXrwwQd10003SbpyomTdunWSpIkTJ1p9QmT37t364IMPtGvXLv366686f/68mjdvrg4dOigwMFChoaHq3r17pXWGDh2qXbt2SZJeeOEF47ETe/fu1Zo1axQfH6/U1FQ5OTnJz89PgYGBmjJlijp37lyl3+O7777T6tWrtWPHDqWmpqpRo0Zq27atbrnlFj300EP685//XKV6VzObzYqOjtZHH32kvXv36syZM8rKylKLFi3UqVMn/e1vf9OkSZPUrl27Smv5+/vrl19+kSS99dZbCg0N1eXLlxUVFaXIyEh99dVXOnPmjLKzs3XnnXcqKiqq2n1XplohMTQ01Hh+IAAAAID64ZVXXtFLL72kgoICi+VJSUlKSkrSunXr9NBDD2np0qVVrn38+HE99thjio2NLbUuPT1d6enpSkhI0KuvvqonnnhCixcvlrOz9XEjLy9PYWFhevPNN0utO3LkiI4cOaKVK1dqxYoVmjRpklU1X3jhBc2bN09FRUUWyzMzM3XkyBGtWLFCs2fP1osvvmh1nyUOHjyoxx9/vMyTXWlpaUpLS1N8fLwWLlyo5557zphg01rJycm69957FW+HqyJqdLlpTWYhBQAAAGA7M2fO1MKFCy2W+fr6qkuXLiosLNTRo0eVmZmpN998U3l5eXJxcbG69r59+/T3v/9d586dM5Y1adJEPXr0UPPmzfXbb7/pu+++U1FRkS5fvqwlS5bo2LFj+uijj6wKisXFxQoODta2bdskSS1atFC3bt3UuHFjJSUl6cyZM5KkwsJCPfTQQ2rXrp2GDx9eYc2pU6cqPDzcYpmfn5+uvfZa5ebm6ttvv1V+fr5efvllFRcXW/1bSNKHH36o8ePH6+LFi8YyDw8PXXfddXJ3d1daWpqOHDkis9msixcv6tlnn1VycrLeeOMNq+qfP39egYGB+vnnnyVJrVu3VufOnWUymcqcbNPWajRxjclksukLAAAAQNV98sknFgGxY8eO+vTTT/Xrr79q9+7d2rdvn86ePas333xTnp6e2rBhg6Kjo62qnZqaqlGjRhkBsV27dnr33XeVmZmpQ4cOKTY2VomJiTp79qyeeeYZ4//rP/nkE+MS0sqsWLFC27Ztk7+/vz788EOlp6crPj5eX3zxhX799VdFRkaqWbNmxvinnnqqwhNWmzdvtgiIXbp00eeff66TJ0/qiy++0MGDB5WWlqZZs2bJZDJp/vz5+u6776zq9auvvtK4ceOMgNijRw9t3bpV58+f1/79+xUbG6vvvvtOqampFs9+X716dZlnScsyZ84c/fzzz+rRo4diYmJ05swZ7d27V3Fxcfr111+1ePFiq+pUV41CotlstukLAAAAQNWYzWaLZ3b/6U9/0u7duzV8+HCLEzGNGzfWpEmT9Mknn8jFxUXp6elW1X/44YeVkZEhSerZs6cSExN17733qnHjxhbjvL29tXDhQq1cudJYtmjRIp06darSfWRkZMjf319ffvmlRo0aJSen32OKyWRScHCwxTPLjx07pri4uDJrFRYWWvwe/v7+2rNnj4YOHWoxzsvLS/PmzdNrr70ms9lscZa0PMXFxbr33nt16dIlSVfuq0xISNAdd9yhRo0aWYz905/+pLVr12rWrFnGslmzZlmcfSxPVlaWevbsqb179yowMNDin6PJZFKnTp0qrVETNnkEBmcSAQAAAPvYuXOnjh8/bnxevHhxhROlDBo0SE899ZRVtb/66it9+umnkiRnZ2dFRkbKx8enwm0eeeQRDRs2TNKVwHZ1uKvIG2+8oTZt2pS7PiQkRG3btjU+79mzp8xxUVFR+vXXX43PS5curbBuWFiYBg8ebFWPH330kY4cOSLpSsiMjIys9PF6c+fOVZcuXSRdCcP/+c9/rNrX6tWr1bx5c6vG2ppNQiJnEgEAAAD7+Pjjj433rVq10pgxYyrd5vHHH7eq9tUzno4YMUI9e/a0aruJEyca72NiYiod36VLF912220VjnFyctKQIUOMz99//32Z4z744APjfceOHXXHHXdUun9rQ/PVv8eECRPUunXrSrdp1KiR7rvvPuOzNb/H9ddfr7/+9a9W9VQbqhUSfX19jfcmk0mjRo1ScXFxjV7z58+32ZcCAAAA/lvs37/feH/TTTdZNVFM586d1b59+0rHlTyiQlKlIe5qffr0Md4fOnSo0hNCAQEBVtW9+gzphQsXyhxz9e9x++23W1V3xIgRlV7ZaDabLc5eVvf3SEhIqHR8yWNK7KVas5sOGDBAW7dulclkktlstuqLAgAAALC9kmfrSdJ1111n9XY9evTQyZMny11vNpstJnNZu3atMftoZa6+766goEBZWVny8vIqd/zVJ6Eq4ubmZrzPy8srtb6oqEgnTpwwPvfu3duquh4eHvL397fY9o9SU1P122+/GZ/nz5+vZcuWWVX/6u2suRe0tu85rEyNQmKJ06dP6/Tp07rmmmts1hgAAACAyl19Rq0q97BVNjYzM9Pi+YJfffVV1Rr7Q62KQqKrq2uVa5Z1dvL8+fMWn1u2bGl1vZYtW1YYEv84sc2+ffusrn21zMzMSsd4enpWq7atVOty0/79+5dadvDgwRo3AwAAAKB+yM3NtVmtqj6HsLoKCgosPv9xBtaKVBZUbfV7WDMXy9Wzu9pDjUNiybW7XHIKAAAA1L2rzwiWd59eWSob+8czje+99161J6j09/e3uq+a+OMZuOzsbKu3zcrKqnD9H3+PAwcOOOyEndUKiT4+PurQoYPFF7VVSGwIPxoAAABQX3To0MF4f/ToUau3K3mUQ3nc3Nzk7u5ufE5LS6t6c3XMw8NDzZo1Mz5XdPno1cxms5KTkysc88f7JhvC71Fd1T6PGRMTo8TEROP12muv1aiRGTNmWMx2evny5RrVAwAAAP4b/OUvfzHe79692+I+wvIcP368wklrSgwaNMh4X9178Opav379jPdXz3RakSNHjlR61rFly5bq2rWr8bmh/B7VUe2Q2KlTJ/Xp08d4devWzZZ9AQAAALDC1c8BTE9P1/vvv1/pNitWrLCq9ogRI4z3H374YanJW+qjm2++2XgfExOjjIyMSrdZv369VbWv/j3+7//+T4WFhVVvsAGw7x2RAAAAAGrktttuU+fOnY3PTz/9tE6dOlXu+Pj4eC1dutSq2pMmTVKLFi0kXZm45YknnqhZs3UgNDTUmDeloKBAzz//fIXjU1NTrf49pk6dKhcXF0lSSkqKnnvuuZo1W08REgEAAIAGzGQyacmSJcbnU6dOaciQIdq+fbvFfB8FBQVau3atRowYocLCQrVq1arS2h4eHpo3b57xOTIyUuPGjSv1qImyJCQkaMKECdqwYUMVv1HNdO7cWf/4xz+MzytXrtTrr79e5tgzZ85o5MiRysnJsaq2v7+//vnPfxqfFy1apGnTplk8F7IsxcXF+vzzz3XnnXdq9+7dVu3Lnqr1nEQAAAAA9ceIESP0zDPPaNGiRZKuTNhy++2365prrlGXLl1UWFioI0eOGM/oCwkJkYuLi9atWyep4sc/PProo0pMTNSqVaskXQmK27Zt0z/+8Q8NGTJEbdu2laurqzIzM3Xy5El99dVX2rlzpzERzLBhw2rxm5ctPDxcn3/+uTG5zLRp0xQVFaX7779fnTp1Um5uruLi4vTGG2/ot99+U9euXeXh4aFDhw5VWvuVV17RN998o+joaEnS66+/rnfffVfjxo3ToEGD5Ovrq0aNGunChQv6+eeflZiYqO3bt1v0Ut8REgEAAAAHsHDhQnl4eGju3LnG8wJPnz6t06dPW4ybNGmSIiIiFBoaaiyr6EH30pV7GP38/PS///u/Ki4uVk5OjtasWaM1a9bY/HvYQuvWrRUTE6NbbrnFuCdx165d2rVrV6mxrVq10qZNmzRlyhSrajs5OSkqKkrTpk3TsmXLJEkZGRmKiIhQRESE7b6EHVXrctO7777b4hUVFWXjtgAAAABU1XPPPadvvvlG06ZNU48ePeTh4SE3Nzd17dpVEydO1K5du/Tmm2+qSZMmFo9wqOzSU5PJpGeffVbffvut7r33XovHTJTF29tbY8aM0ebNmxUSEmKT71ZVvXr10jfffKPg4GA1atSo1PpGjRrpjjvuUGJioq6//voq1XZxcVFERIT27dunv//972rcuHGF4319fTVhwgR9+umnGjJkSJX2ZQ/VOpMYFRVl3AwqSQMHDixz3B//YcyfP1/PPPNMdXYJAAAAwArdunXTv//97wrHFBUVWVxa2bdvX6tq9+jRQ++++64KCgq0f/9+HT9+XBkZGSosLJS7u7vatm2r7t2767rrrpOTU8Xno7744gur9nm1OXPmaM6cOVaPv+aaaxQZGamzZ8/qs88+U2pqqho1aqS2bdtqyJAhuuaaa2rUz8CBA/XRRx8pLy9P8fHxSk5O1rlz51RcXCwPDw+1b99e1113nbp06VJprcqe01iXanS5qdlstgiLZa0vUdE4AAAAAHUnMjLSeC5g48aNNWDAgCpt37hxYw0ZMqRBnBWTrlx+Om7cuFqr36xZM9166621Vr+u1frspoRDAAAAoPZdfYKmIsnJyZo+fbrxecyYMZXek4j/LjwCAwAAAHAAzz77rB566CHFxsYaE9dcLScnRytXrlT//v119uxZSVKTJk00e/bsum4V9RyzmwIAAAAOIC8vz5hx1MXFRV27djUmpMnIyNDRo0d1+fJlY3zJ8xV79uxpr5ZRTxESAQAAAAdw9UQxhYWF+v7778sd6+vrq+XLl+uuu+6qi9bQwBASAQAAAAewcOFCDR8+XDExMUpISNDPP/+sjIwMXbp0SR4eHmrVqpVuuOEG3XbbbQoJCVHTpk3t3TLqKUIiAAAA4ABcXFw0fPhwDR8+3N6toIFj4hoAAAAAgIGQCAAAAAAw2ORy0/Pnz+vkyZM2G1eiffv2NWkLAAAAAFBFNQ6JZrNZixYt0qJFi8pdb824PzKZTCoqKqppewAAAACAKrDJmcSSIGircQAAAAAA+7BJSDSZTGUu/2MoLG9cZdsBAAAAAOpGvTuTaG2QBAAAAADYXo1CIoEOAAAAABxLtUMil4QCAAAAgOOpVkicOHGirfsAAAAAANQD1QqJb731lq37AAAAAADUA072bgAAAAAAUH84bEjctGmThg4dKm9vb7m5ualPnz5atGiRCgsLa1w7OjpaJpNJJpNJt956qw26BQAAAID6wSFD4tSpUxUcHKy9e/fqxhtv1O23366TJ09qxowZGjZsmC5evFjt2ufPn9fDDz/MzK4AAABAPTVnzhzjpI7JZNLGjRsr3WbkyJEW2yQnJ1d5v/7+/hY1ynuFhoZabFdUVKTY2Fj961//0rhx49S1a1c5OTmVObYu2OQ5ifVJVFSUwsPD5e7url27dqlfv36SpIyMDA0bNkxxcXF6/vnn9eqrr1ar/lNPPaW0tDQ99thjWrFihS1bBwAAAKyy7ftke7dgEyN7+tfJft566y2NGzeu3PW//vqrtm/fbrP9NWnSRF5eXuWu/+O61NTUenWFosOdSZw3b54kaebMmUZAlCQfHx8tX75ckhQREaHMzMwq196yZYvWr1+v6dOn68Ybb7RNw2iwmprNalZcrGbFxWrKI2EAAADqHR8fH7m5uSkmJkapqanljnvnnXd0+fJl+fv722S///jHP3TmzJlyX+Hh4aW28fDw0ODBgxUWFqZ169bpz3/+s016qQ6HComnTp3SwYMHJUkhISGl1g8ePFh+fn7Kz89XdHR0lWpnZGToscceU7du3fTSSy/ZpF80bLcUFCgoP19B+fm6paDA3u0AAADgD9zc3DRmzBgVFxfr7bffLndcydMb7HFppyS1b99emZmZ2rNnj15//XVNmDChwjORtc2hQmJiYqIkqUWLFurYsWOZY/r3728x1lqPP/64MjIytGbNGjVp0qRmjQIAAACoEw888IAklRsS4+Li9OOPP+raa6/VTTfdVIed/a7k/sP6wqFC4okTJyRdSeLl8fPzsxhrjY0bN+r999/XU089pYCAgJo1CQAAAKDO3HTTTerUqZN++ukn7d69u9T6q88i1qegZk8OFRKzs7MlXTmtXB53d3dJUlZWllU1z5w5oyeeeEKdOnUy7nesjvz8fGVlZVm8AAAAANSuq2cIXbt2rcW63Nxcvffee3JycrLbpab1kUOFxNrwyCOP6Pz583rzzTfVrFmzateZP3++vLy8jFfJGU0AAAAAtWvixIlycnLS+++/r5ycHGP5e++9p5ycHAUGBtr0/88jIyPl6+tb5qs+zWJaHocKiR4eHpKu/I1AeUoOCk9Pz0rrrVu3Tlu3btVjjz2moUOH1qi3WbNmKTMz03ilpKTUqB4AAAAA6/j5+enWW281zhyWKLnU9MEHH7Tp/i5duqS0tLQyXxkZGTbdV21wqOcklkxZW1EAK1lnzfS2W7ZskSQdPHiwVEg8c+aMJOnQoUPGuo0bN8rX17fMWq6urnJ1da10nwAAAABs74EHHtCOHTu0du1aPfjggzp+/Lj27Nkjb29vjR492qb7mjhxYoWzqdZ3DhUS+/btK0k6d+6cTpw4UeYMpwkJCZJk8QzFypRsU5YLFy5o165dkq78jQEAAACA+ueuu+6St7e39u7dq2PHjhkhbvz48Ty94A8c6nLTdu3aacCAAZKkDRs2lFofFxenlJQUubq6KigoqNJ6UVFRMpvNZb5KTk0HBgYay2z18E0AAAAAtuXq6qrx48dLkt5880298847kn5/RAZ+51AhUZJmz54tSVqwYIEOHz5sLD937pwmT54sSXryySctHk65ZcsWde/eXYGBgXXbLAAAAIA6UxIIX3/9daWmpqpXr17Gc9TxO4cLiaNHj9aUKVOUk5OjgQMHasSIERozZow6d+6sb7/9VgEBAZo7d67FNpmZmUpKStJPP/1kp64BAAAA1Lb+/furd+/eKigokGT7CWschUPdk1giPDxcAQEBWrZsmeLj41VYWKhOnTpp5syZmjZtmho3bmzvFgEAAADYwcKFCxUbGytJuu++++zcTf3kkCFRkoKDgxUcHGzV2NDQ0Co/PLM62wAAAACwrxEjRmjEiBH2bqOUzMxMFRYWGp9L3ufn51s8NsPFxcXi1rna4LAhEQCAuube3LvM9wAAVObOO+80nppwtY0bN2rjxo3G55tvvllffPFFrfZCSAQAwEbGTZ1l7xYAAKgxQiIAAADQwIzs6W/vFuq1OXPmaM6cOVXebujQoTKbzdXeb3JycrW3re2zg1XhcLObAgAAAACqj5AIAAAAADBwuSkAALCZpmazVFz8+/v/Ytu+T7bbvi8WFlm8t2cvABoeQiIAALCZW/7/A6oBAA0Xl5sCAAAAAAyERAAAAACAgZAIAAAAADAQEgEAAAAABkIiAAAAAMBASAQAAAAAGAiJAAAAAAADIREAAAAAYCAkAgAAAAAMhEQAAAAAgIGQCAAAAAAwEBIBAAAAAAZCIgAAAADAQEgEAAAA4FDmzJkjk8lk1auqrK07Z84ci+3y8vL0ySef6OWXX9bdd9+tDh06lDvW3pzt3QAAAACAqlkxaJC9W7CJx+Pja30fbdq0qZW6bm5ucnd3L3f9H9cdOHBAQUFBtdKLrRESAQAAADisM2fO1Erdp59+uspnAL29vdWvXz/jNW3atFrrryYIiQAAAABQy4YMGaLffvvNYtnMmTPt1E3FuCcRAAAAAGpZo0aN7N2C1TiTCAAAAIfT1GyWiot/fw/AaoREAAAAOJxbCgrs3QLQYBESAQAAADgsX1/fctfFxsaqZ8+e1ar76quvauXKlWWuGzFihN56661q1a0PCIkAAAAAHFZaWlq56woLC6tdNzc3V7m5uWWuO3/+fLXr1geERAAAAAAOy1xL96S+8MILVX4ERkPB7KYAAAAAAAMhEQAAAABgICQCAAAAAAyERAAAAACAgZAIAAAAADAwuykAAAAA1IHz58/r8uXLxufi4mJJUl5enjIyMozlTZo0kbu7e533V4IziQAAAABQB/r27atWrVoZr5SUFEnSv/71L4vlTz75pF37JCQCAAAAAAxcbgoAAADAocyZM6fWHnRvNpurvW1ycrLtGqlFhEQAAACggXk8Pt7eLcCBcbkpAAAAAMDgsCFx06ZNGjp0qLy9veXm5qY+ffpo0aJFKiwsrFKdxMREzZ8/X4GBgWrTpo1cXFzk7e2tIUOGaNmyZVWuBwAAAAD1mUNebjp16lSFh4fL2dlZw4YNk7u7uz777DPNmDFDW7du1Y4dO9S0adNK6xQVFalfv36SJHd3dw0YMEBt2rRRamqq9u3bp7i4OL3zzjvavn27mjdvXsvfCgAAAABqn8OdSYyKilJ4eLjc3d21f/9+bd++XZs3b9axY8fUu3dvxcXF6fnnn7e63g033KD33ntPGRkZ+uyzz/Sf//xHe/bsUWJioq655hodOHBA06dPr8VvBAAAAAB1x+FC4rx58yRJM2fONM4CSpKPj4+WL18uSYqIiFBmZmaltZydnZWQkKCxY8fK1dXVYl3v3r21aNEiSdLGjRu57BQAAACAQ3CokHjq1CkdPHhQkhQSElJq/eDBg+Xn56f8/HxFR0fXeH99+/aVJF28eFEZGRk1rgcAAAAA9uZQITExMVGS1KJFC3Xs2LHMMf3797cYWxPHjh2TJDVu3FgtWrSocT0AAAAAsDeHCoknTpyQJLVv377cMX5+fhZjq8tsNhuXm95xxx2lLkcFAAAAgIbIoWY3zc7OliS5ubmVO8bd3V2SlJWVVaN9vfjii9q3b5/c3d21YMGCSsfn5+crPz/f+FzT/QMAAABAbXCoM4l15Z133tFLL70kJycnrV27Vl26dKl0m/nz58vLy8t4lZzRBAAAAID6xKFCooeHhyQpNze33DE5OTmSJE9Pz2rtY9OmTXrwwQclSatXr9bYsWOt2m7WrFnKzMw0XikpKdXaPwAAAADUJoe63NTf31+SKgxgJetKxlbFBx98oJCQEBUXF2vVqlVGWLSGq6sr9y0CAAAAqPcc6kxiySMpzp07V+7ENAkJCZJk8QxFa0RFRWncuHG6fPmyVqxYoYcffrhmzQIAAABAPeRQIbFdu3YaMGCAJGnDhg2l1sfFxSklJUWurq4KCgqyuu7WrVsVHBysoqIirVixQo8++qjNegYAAACA+sShQqIkzZ49W5K0YMECHT582Fh+7tw5TZ48WZL05JNPysvLy1i3ZcsWde/eXYGBgaXqRUdHa8yYMSoqKtLKlSsJiAAAAAAcmkPdkyhJo0eP1pQpU7RkyRINHDhQgYGBcnNzU2xsrC5cuKCAgADNnTvXYpvMzEwlJSXp0qVLFsvPnj2ru+++WwUFBWrXrp3i4+MVHx9f5n5fffVV+fj41Nr3AgAAAIC64HAhUZLCw8MVEBCgZcuWKT4+XoWFherUqZNmzpypadOmqXHjxlbVycvLM55tmJqaqnXr1pU7ds6cOYREAAAAAA2eQ4ZESQoODlZwcLBVY0NDQxUaGlpqub+/v8xms407AwAAAID6y+HuSQQAAAAAVB8hEQAAAABgICQCAAAAAAyERAAAAACAgZAIAAAAADAQEgEAAAAABkIiAAAAAMBASAQAAAAAGAiJAAAAAAADIREAAAAAYCAkAgAAAAAMhEQAAAAAgIGQCAAAAAAwEBIBAAAAAAZCIgAAAADAQEgEAAAAABgIiQAAAAAAAyERAAAAAGAgJAIAAAAADIREAAAAAICBkAgAAAAAMBASAQAAAAAGQiIAAAAAwEBIBAAAAAAYCIkAAAAAAAMhEQAAAABgICQCAAAAAAyERAAAAACAgZAIAAAAADAQEgEAAAAABkIiAAAAAMBASAQAAAAAGAiJAAAAAAADIREAAAAAYCAkAgAAAAAMhEQAAAAAgIGQCAAAAAAwEBIBAAAAAAZCIgAAAADAQEgEAAAAABgIiQAAAAAAg8OGxE2bNmno0KHy9vaWm5ub+vTpo0WLFqmwsLBa9Q4dOqSxY8eqTZs2atKkiTp27KinnnpKZ8+etXHnAAAAAGA/DhkSp06dquDgYO3du1c33nijbr/9dp08eVIzZszQsGHDdPHixSrVe//99zVw4EC9//776tChg+688045OTkpIiJC119/vY4fP15L3wQAAAAA6pbDhcSoqCiFh4fL3d1d+/fv1/bt27V582YdO3ZMvXv3VlxcnJ5//nmr6/3666+aOHGiioqKtGrVKh04cECRkZH68ccfdd999yktLU0hISEym821+K0AAAAAoG44XEicN2+eJGnmzJnq16+fsdzHx0fLly+XJEVERCgzM9Oqeq+//rry8vJ066236pFHHjGWN2rUSCtWrJCXl5cOHjyoHTt22PBbAAAAAIB9OFRIPHXqlA4ePChJCgkJKbV+8ODB8vPzU35+vqKjo62quWXLlnLrubu7a9SoUZKkDz74oLptAwAAAEC94VAhMTExUZLUokULdezYscwx/fv3txhbkezsbON+w5LtalIPAAAAAOo7hwqJJ06ckCS1b9++3DF+fn4WYyuSnJxsvC+vZlXqAQAAAEB952zvBmwpOztbkuTm5lbuGHd3d0lSVlaW1fUqqmltvfz8fOXn5xufS+6JtKaP8uTlZFc+yMFdLCqydwv1Qk2OI1vheOR4lDgW6wuOxSvsfTza81gsLCxU0f8/DgoLC+3aC8ejbY5FDw8PmUwmG3QDVM6hQmJ9Nn/+fL344oullpeciQRq4p9eXvZuAZDEsYj6hePxd9s+jLJ3C//VbHEsZmZmytPT0wbdAJVzqJDo4eEhScrNzS13TE5OjiRZ9S9ZSb2Sml5l/Atubb1Zs2Zp+vTpxufi4mL99ttvatmyJX8rVE1ZWVny8/NTSkoK/9GE3XE8or7gWER9wvFoO1f/fylQ2xwqJPr7+0uSUlJSyh1Tsq5kbEU6dOhgvD958qR69+5d7Xqurq5ydXW1WNa8efNKe0DlPD09+YMH9QbHI+oLjkXUJxyPQMPiUBPX9O3bV5J07ty5cieSSUhIkCSLZyiWx9PTU507d7bYrib1AAAAAKC+c6iQ2K5dOw0YMECStGHDhlLr4+LilJKSIldXVwUFBVlV86677iq3Xk5OjrZu3SpJuvvuu6vbNgAAAADUGw4VEiVp9uzZkqQFCxbo8OHDxvJz585p8uTJkqQnn3zS4v7CLVu2qHv37goMDCxVb+rUqWrWrJliYmK0evVqY/nly5c1efJkXbhwQQMGDNDf/va32vpKKIerq6teeOGFUpfxAvbA8Yj6gmMR9QnHI9Awmcxms9neTdhaWFiYlixZIhcXFwUGBsrNzU2xsbG6cOGCAgICtHPnTjVt2tQY//bbb+uBBx5Qhw4dLJ6NWGLTpk0aP368Ll++rL/85S/y9/fXwYMH9fPPP6tNmzaKi4szLksFAAAAgIbM4c4kSlJ4eLgiIyP117/+VfHx8YqOjla7du20YMECffbZZxYB0Rpjx47V/v37dffdd+vnn3/Wli1bdPnyZT3xxBP6+uuvCYgAAAAAHIZDnkkEAAAAAFSPQ55JRMOSlJSkpUuXKjQ0VL1795azs7NMJpNefvnlGtWNiYlRUFCQfHx81LRpU3Xv3l3PPvus8WxL4I8KCwsVGxur//mf/9GAAQPUvHlzubi4yNfXV6NGjdK2bduqXZvjEVW1fv16TZgwQX369FHr1q3l4uIiLy8v3XjjjZo/f361jx2ORdjCM888I5PJVKM/rzkWgXrMDNhZWFiYWVKp19y5c6td89///rdZktlkMplvuukm89ixY82+vr5mSeZu3bqZ09PTbfgN4Ch27txpHH++vr7mkSNHmoODg829evUylj/yyCPm4uLiKtXleER1BAQEmE0mk7lHjx7m4cOHm8ePH28eNmyYuWnTpmZJ5s6dO5tPnTpVpZoci7CFvXv3mp2cnMwmk6naf15zLAL1GyERdrd69Wrz008/bV6/fr356NGj5vvvv79GIfHw4cNmk8lkbtSokTk6OtpYnpubaw4MDDRLMt9zzz22ah8OJDY21nzPPfeYd+/eXWrdxo0bzY0aNTJLMq9bt87qmhyPqK4vv/zSfO7cuVLLMzIyzIMHDzZLMo8bN87qehyLsIXc3Fxzly5dzG3btjWPHj26Wn9ecywC9R8hEfXOxIkTaxQSx44da5Zkfuihh0qtS05ONjs5OZklmY8ePVrTVvFfZtKkSWZJ5sDAQKu34XhEbdi9e7dZkrlFixZWb8OxCFuYMmWKWZJ527Zt1f7zmmMRqP+4JxEOpaCgwLhvLCQkpNT6Dh06KCAgQNKV52MCVdG3b19JUkpKilXjOR5RW5ydnSXJ6mfPcSzCFr744gstXbpUEyZMUFBQULVqcCwCDQMhEQ7lxx9/VF5eniSpf//+ZY4pWZ6YmFhnfcExHDt2TJJ0zTXXWDWe4xG1ITs7W3PmzJEkjRo1yqptOBZRUzk5OXrwwQfVpk0bvf7669Wuw7EINAzO9m4AsKUTJ05Ikpo3by4PD48yx/j5+VmMBaxx5swZvf3225Kke+65x6ptOB5hCzt27NCGDRtUXFystLQ07du3T9nZ2br99tu1cOFCq2pwLKKmnn76aZ04cUJbtmyRt7d3tetwLAINAyERDiU7O1uS5ObmVu4Yd3d3SVJWVlad9ISGr6ioSPfdd58yMzPVu3dvPfroo1Ztx/EIWzhy5IjWrVtnsSwkJET//ve/5eXlZVUNjkXUxI4dO7Rq1SqNGzdOo0ePrlEtjkWgYeByUwCoxGOPPabY2Fi1bNlS77//vho3bmzvlvBfZOrUqTKbzSooKNDx48e1ePFiffLJJ+rRo4d2795t7/bg4DIzMzVp0iS1atVKS5cutXc7AOoIIREOpeTSldzc3HLHlDyk19PTs056QsMWFhamNWvWyNvbWzt37lTXrl2t3pbjEbbk4uKiTp06afr06frkk090/vx53Xfffbp48WKl23IsorqmTp2q1NRURUREyMfHp8b1OBaBhoHLTeFQ/P39JUkXLlxQdnZ2mfc7lMxMWTIWKM8///lPLVmyRM2bN9eOHTuM2U2txfGI2vKXv/xFPXr00Pfff6+EhAQNGTKkwvEci6iuLVu2yNnZWcuXL9fy5cst1v3www+SpDVr1igmJka+vr7auHFjhfU4FoGGgZAIh9KtWzc1a9ZMeXl5SkhI0C233FJqTEJCgiSpX79+dd0eGpBnnnnGuOdrx44d5c7CVxGOR9Smknu6zp49W+lYjkXURFFRkXbt2lXu+uTkZCUnJ6tDhw6V1uJYBBoGLjeFQ2ncuLFGjhwpSdqwYUOp9b/88ovi4+MlSXfddVed9oaGY+bMmfrXv/4lLy8v7dy5UwMGDKhWHY5H1JaMjAx9/fXXkmTVJdAci6iuCxcuyGw2l/maOHGiJGnu3Lkym81KTk6utB7HItAwEBLRIEVERKh79+6aMGFCqXUzZ86UyWTSW2+9pU8//dRYnpeXp0mTJuny5cu655571L1797psGQ3Ec889p4ULF6p58+ZWB0SOR9jakSNHtH79el26dKnUuh9//FFjx45Vfn6+Bg4cqN69exvrOBZRX3AsAg0bl5vC7g4fPqzJkycbn3/66SdJ0qpVq/Txxx8by7ds2WI8xDwjI0NJSUny9fUtVa9fv35avHixpk+frqCgIN18881q3bq19uzZo9OnT6tbt25auXJlLX8rNEQfffSRXnnlFUlS586dtWzZsjLH+fj46NVXXzU+czzC1s6ePav77rtPjz76qPr27at27dqpoKBAJ0+e1OHDh1VcXKzrrrtOkZGRFttxLKK+4FgEGjZCIuwuKytL+/fvL7U8NTVVqampxuf8/Hyra06bNk29e/fW4sWLdeDAAeXm5qp9+/aaNWuWZs2aVe4DfPHf7bfffjPeJyQkGPfF/FGHDh0sQmJlOB5RVT179tQrr7yiPXv26IcfflBiYqIKCwvVokULBQYG6u6779YDDzwgV1fXKtXlWER9wbEI1G8ms9lstncTAAAAAID6gXsSAQAAAAAGQiIAAAAAwEBIBAAAAAAYCIkAAAAAAAMhEQAAAABgICQCAAAAAAyERAAAAACAgZAIAAAAADAQEgEAAAAABkIiAAAAAMBASAQASJJCQ0NlMpkqfEVHR1td74cffpCTk1OF9UJDQ2vU8xdffFFpzzV9AQDw34aQCACw2pIlS6o01mw212I3AACgNhASAQBW27Fjh5KSkiodd+HCBb3zzjt10BEAALA1QiIAwGpms1lLly6tdNyaNWuUm5tbBx0BAABbc7Z3AwCAhmXdunWaN2+ePD09y1xfXFysiIiIOunl2muv1fz58yscExMTo9jY2DLXPfzww7r22mtrozUAABosQiIAoEpycnL01ltvKSwsrMz1H374oZKTk+ukl/bt22vmzJkVjrl06VK5ITEkJERDhw6thc4AAGi4uNwUAFBlERER5U5KU5XJbQAAQP1DSAQAVKht27Zq166dxbLjx4+X+TiMb775Rl988YXFsmbNmun666+vzRYBAIANERIBABVydnbW5MmTSy0vawKbss4i3n///fL29q6V3gAAgO0REgEAlXrkkUfUpEkTi2V/fBxGRkaG1q9fX2rbKVOm1Hp/AADAdgiJAIBKtWzZUvfee6/Fsj8+DuONN97QpUuXLMbceuut6tGjR530CAAAbIOQCACwSllnBNetW6esrCwVFRVpxYoVVm0DAADqN0IiAMAq119/vW6++WaLZSWPw9i8ebNSU1Mt1nXq1EkjR46syxYBAIAN8JxEAIDVwsLCtGvXLotlERER8vHxKTX2iSeekJMTfxcJAEBDw5/eAACrjRo1Sh06dLBYdvz4cX355ZcWy9zd3fXggw/WZWsAAMBGCIkAAKs1atRITzzxRKXjJk6cKC8vrzroCAAA2BohEQBQJQ899JCaNWtW7nqTyaSnnnqqDjsCAAC2REgEAFSJt7e37r///nLXDx8+XN26davDjgAAgC0REgEAVVbRoy147AUAAA0bIREAUGU9evRQYGBgqeVdu3bV7bffboeOAACArRASAQDVEhYWVmrZU089JZPJZIduAACArfCcRABAtfz9739XYWGhxTJnZ/5YAQCgoeNPcwBAtREKAQBwPFxuCgAAAAAwEBIBAAAAAAZCIgAAAADAYDKbzWZ7NwEAAAAAqB84kwgAAAAAMBASAQAAAAAGQiIAAAAAwEBIBAAAAAAYCIkAAAAAAAMhEQAAAABgICQCAAAAAAyERAAAAACAgZAIAAAAADAQEgEAAAAABkIiAAAAAMBASAQAAAAAGAiJAAAAAAADIREAAAAAYCAkAgAAAAAMhEQAAAAAgOH/AcMcrCd6AcY0AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 960x450 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "graph_5_delta_g(\"../data/de_results_mbart.txt\", tokens_dict_de,\"German\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "9b3f1d15",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_15240/2078070108.py:41: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta G\": male_f1 - female_f1,\n",
      "/tmp/ipykernel_15240/2078070108.py:41: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta G\": male_f1 - female_f1,\n",
      "/tmp/ipykernel_15240/2078070108.py:41: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta G\": male_f1 - female_f1,\n",
      "/tmp/ipykernel_15240/2078070108.py:41: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta G\": male_f1 - female_f1,\n",
      "/tmp/ipykernel_15240/2078070108.py:41: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta G\": male_f1 - female_f1,\n",
      "/tmp/ipykernel_15240/2078070108.py:41: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta G\": male_f1 - female_f1,\n",
      "/tmp/ipykernel_15240/2078070108.py:41: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta G\": male_f1 - female_f1,\n",
      "/tmp/ipykernel_15240/2078070108.py:41: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta G\": male_f1 - female_f1,\n",
      "/tmp/ipykernel_15240/2078070108.py:41: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta G\": male_f1 - female_f1,\n",
      "/tmp/ipykernel_15240/2078070108.py:41: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta G\": male_f1 - female_f1,\n",
      "/tmp/ipykernel_15240/2078070108.py:41: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta G\": male_f1 - female_f1,\n",
      "/tmp/ipykernel_15240/2078070108.py:41: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta G\": male_f1 - female_f1,\n",
      "/tmp/ipykernel_15240/2078070108.py:41: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta G\": male_f1 - female_f1,\n",
      "/tmp/ipykernel_15240/2078070108.py:41: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta G\": male_f1 - female_f1,\n",
      "/tmp/ipykernel_15240/2078070108.py:41: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta G\": male_f1 - female_f1,\n",
      "/tmp/ipykernel_15240/2078070108.py:41: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta G\": male_f1 - female_f1,\n",
      "/tmp/ipykernel_15240/2078070108.py:41: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta G\": male_f1 - female_f1,\n",
      "/tmp/ipykernel_15240/2078070108.py:41: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta G\": male_f1 - female_f1,\n",
      "/tmp/ipykernel_15240/2078070108.py:41: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta G\": male_f1 - female_f1,\n",
      "/tmp/ipykernel_15240/2078070108.py:41: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta G\": male_f1 - female_f1,\n",
      "/tmp/ipykernel_15240/2078070108.py:41: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta G\": male_f1 - female_f1,\n",
      "/tmp/ipykernel_15240/2078070108.py:41: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta G\": male_f1 - female_f1,\n",
      "/tmp/ipykernel_15240/2078070108.py:41: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta G\": male_f1 - female_f1,\n",
      "/tmp/ipykernel_15240/2078070108.py:41: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta G\": male_f1 - female_f1,\n",
      "/tmp/ipykernel_15240/2078070108.py:41: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta G\": male_f1 - female_f1,\n",
      "/tmp/ipykernel_15240/2078070108.py:41: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta G\": male_f1 - female_f1,\n",
      "/tmp/ipykernel_15240/2078070108.py:41: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta G\": male_f1 - female_f1,\n",
      "/tmp/ipykernel_15240/2078070108.py:41: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta G\": male_f1 - female_f1,\n",
      "/tmp/ipykernel_15240/2078070108.py:41: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta G\": male_f1 - female_f1,\n",
      "/tmp/ipykernel_15240/2078070108.py:41: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta G\": male_f1 - female_f1,\n",
      "/tmp/ipykernel_15240/2078070108.py:41: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta G\": male_f1 - female_f1,\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 700x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "correlation\n",
      "          Delta T   Delta G      M F1      F F1       M T       F T\n",
      "Delta T  1.000000 -0.070572  0.034638  0.092046  0.323732 -0.273488\n",
      "Delta G -0.070572  1.000000  0.313665 -0.822934 -0.038498  0.003390\n",
      "M F1     0.034638  0.313665  1.000000  0.281340  0.069051  0.049326\n",
      "F F1     0.092046 -0.822934  0.281340  1.000000  0.080222  0.026088\n",
      "M T      0.323732 -0.038498  0.069051  0.080222  1.000000  0.821540\n",
      "F T     -0.273488  0.003390  0.049326  0.026088  0.821540  1.000000\n",
      "    M T gender        F1\n",
      "0   1.0   M F1  0.714286\n",
      "1   2.0   M F1  0.680412\n",
      "2   2.0   M F1  0.727273\n",
      "3   2.0   M F1  0.965517\n",
      "4   1.0   M F1  0.888889\n",
      "..  ...    ...       ...\n",
      "57  2.0   F F1  1.000000\n",
      "58  1.0   F F1  0.956522\n",
      "59  2.0   F F1  1.000000\n",
      "60  3.0   F F1  1.000000\n",
      "61  4.0   F F1  1.000000\n",
      "\n",
      "[62 rows x 3 columns]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 960x450 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "graph_5_delta_g(\"../data/he_results_mbart.txt\", tokens_dict_he,\"Hebrew\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "598d6003",
   "metadata": {},
   "outputs": [],
   "source": [
    "def graph_5_delta_s(results_file,tokens_dict,lang, male_stereotype):\n",
    "    with open(results_file, \"r\") as f:\n",
    "        lines = f.readlines()\n",
    "        recalls_str = lines[RECALL]\n",
    "        recalls_str = recalls_str.replace(\"'\", \"\\\"\")\n",
    "        recalls_dict = json.loads(recalls_str)\n",
    "\n",
    "        precisions_str = lines[PRECISION]\n",
    "        precisions_str = precisions_str.replace(\"'\", \"\\\"\")\n",
    "        precisions_dict = json.loads(precisions_str)\n",
    "\n",
    "    professions = list(tokens_dict.keys())\n",
    "    delta_t_dict = dict()\n",
    "    \n",
    "    for p in professions:\n",
    "        if p.split('-')[0] in male_stereotype:\n",
    "            delta_t_dict[p] = tokens_dict[p]['Male'] - tokens_dict[p]['Female']\n",
    "        else:\n",
    "            delta_t_dict[p] = tokens_dict[p]['Female'] - tokens_dict[p]['Male']\n",
    "    delta_t_dict = dict(sorted(delta_t_dict.items(), key=lambda item: item[1]))\n",
    "\n",
    "\n",
    "\n",
    "    data = pd.DataFrame({\"Delta T\": [], \"Delta G\": [], \"P F1\": [], \"A F1\": [], \"P T\": [], \"A T\": []})\n",
    "\n",
    "    for prof, delta_t in delta_t_dict.items():\n",
    "        prof = prof.lower()\n",
    "        if prof in recalls_dict and prof in precisions_dict:\n",
    "            r_m,r_f=recalls_dict[prof]['male_recall'],recalls_dict[prof]['female_recall']\n",
    "            p_m,p_f=precisions_dict[prof]['male_precision'],precisions_dict[prof]['female_precision']\n",
    "            if r_m ==0 and p_m == 0:\n",
    "                male_f1 = 0\n",
    "            else:\n",
    "                male_f1= 2 * (r_m*p_m)/(r_m+p_m)\n",
    "    \n",
    "            if r_f ==0 and p_f == 0:\n",
    "                female_f1 = 0\n",
    "            else:\n",
    "                female_f1= 2 * (r_f*p_f)/(r_f+p_f)\n",
    "            \n",
    "            if prof.split('-')[0] in male_stereotype:\n",
    "                # data = data.append({\"Delta T\": delta_t, \"Delta S\": male_f1 - female_f1}, ignore_index=True)\n",
    "                data = data.append({\"Delta T\": delta_t, \"Delta S\": male_f1 - female_f1,\n",
    "                    \"P F1\": male_f1, \"A F1\": female_f1,\n",
    "                    \"P T\": tokens_dict[prof]['Male'], \"A T\": tokens_dict[prof]['Female']},\n",
    "                   ignore_index=True)\n",
    "            else:\n",
    "                # data = data.append({\"Delta T\": delta_t, \"Delta S\": female_f1 - male_f1}, ignore_index=True)\n",
    "                data = data.append({\"Delta T\": delta_t, \"Delta S\": female_f1 - male_f1,\n",
    "                    \"P F1\": female_f1, \"A F1\": male_f1,\n",
    "                    \"P T\": tokens_dict[prof]['Female'], \"A T\": tokens_dict[prof]['Male']},\n",
    "                   ignore_index=True)\n",
    "\n",
    "\n",
    "    f, ax = plt.subplots(figsize=(7, 6))\n",
    "\n",
    "    sns.boxplot(\n",
    "        data=data, x=\"Delta T\", y=\"Delta S\",\n",
    "        orient=\"v\",\n",
    "        notch=False, showcaps=False,showmeans=False,\n",
    "        boxprops={\"facecolor\": (.6, .8, .4, .5)},\n",
    "        medianprops={\"color\": \"coral\"})\n",
    "    sns.stripplot(x=\"Delta T\", y=\"Delta S\", data=data,\n",
    "              size=4, color=\".3\", linewidth=0)\n",
    "    \n",
    "#     z = np.polyfit(data['Delta T'], data['Delta G'], 1)\n",
    "#     p = np.poly1d(z)\n",
    "#     ax.plot(data['Delta T'] + len(data['Delta T'].unique())- 1, p(data['Delta T']), \"r--\")\n",
    "\n",
    "    # Tweak the visual presentation\n",
    "    ax.yaxis.grid(True)\n",
    "    sns.despine(trim=False)\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    plt.savefig(f'../graphs/graph_5_{lang}_delta_s_mbart.pdf')\n",
    "    plt.show()\n",
    "    \n",
    "    print(\"correlation\")\n",
    "    print(data.corr())\n",
    "    \n",
    "    tidy = data.melt(value_vars=['P F1','A F1'], id_vars=['P T'], value_name='F1',var_name='stereotype')\n",
    "    print(tidy)\n",
    "    \n",
    "    sns.catplot(x=\"P T\", y='F1',\n",
    "        data=tidy, kind=\"bar\", hue='stereotype', \n",
    "        palette=sns.color_palette(['mediumaquamarine', 'darkorange']),\n",
    "        height=4.5, aspect=1.8)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "5d092d29",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_15240/351793889.py:43: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta S\": male_f1 - female_f1,\n",
      "/tmp/ipykernel_15240/351793889.py:43: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta S\": male_f1 - female_f1,\n",
      "/tmp/ipykernel_15240/351793889.py:43: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta S\": male_f1 - female_f1,\n",
      "/tmp/ipykernel_15240/351793889.py:43: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta S\": male_f1 - female_f1,\n",
      "/tmp/ipykernel_15240/351793889.py:43: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta S\": male_f1 - female_f1,\n",
      "/tmp/ipykernel_15240/351793889.py:43: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta S\": male_f1 - female_f1,\n",
      "/tmp/ipykernel_15240/351793889.py:43: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta S\": male_f1 - female_f1,\n",
      "/tmp/ipykernel_15240/351793889.py:43: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta S\": male_f1 - female_f1,\n",
      "/tmp/ipykernel_15240/351793889.py:43: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta S\": male_f1 - female_f1,\n",
      "/tmp/ipykernel_15240/351793889.py:43: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta S\": male_f1 - female_f1,\n",
      "/tmp/ipykernel_15240/351793889.py:43: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta S\": male_f1 - female_f1,\n",
      "/tmp/ipykernel_15240/351793889.py:43: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta S\": male_f1 - female_f1,\n",
      "/tmp/ipykernel_15240/351793889.py:49: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta S\": female_f1 - male_f1,\n",
      "/tmp/ipykernel_15240/351793889.py:43: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta S\": male_f1 - female_f1,\n",
      "/tmp/ipykernel_15240/351793889.py:49: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta S\": female_f1 - male_f1,\n",
      "/tmp/ipykernel_15240/351793889.py:49: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta S\": female_f1 - male_f1,\n",
      "/tmp/ipykernel_15240/351793889.py:43: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta S\": male_f1 - female_f1,\n",
      "/tmp/ipykernel_15240/351793889.py:43: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta S\": male_f1 - female_f1,\n",
      "/tmp/ipykernel_15240/351793889.py:43: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta S\": male_f1 - female_f1,\n",
      "/tmp/ipykernel_15240/351793889.py:43: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta S\": male_f1 - female_f1,\n",
      "/tmp/ipykernel_15240/351793889.py:49: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta S\": female_f1 - male_f1,\n",
      "/tmp/ipykernel_15240/351793889.py:49: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta S\": female_f1 - male_f1,\n",
      "/tmp/ipykernel_15240/351793889.py:49: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta S\": female_f1 - male_f1,\n",
      "/tmp/ipykernel_15240/351793889.py:49: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta S\": female_f1 - male_f1,\n",
      "/tmp/ipykernel_15240/351793889.py:49: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta S\": female_f1 - male_f1,\n",
      "/tmp/ipykernel_15240/351793889.py:49: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta S\": female_f1 - male_f1,\n",
      "/tmp/ipykernel_15240/351793889.py:49: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta S\": female_f1 - male_f1,\n",
      "/tmp/ipykernel_15240/351793889.py:49: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta S\": female_f1 - male_f1,\n",
      "/tmp/ipykernel_15240/351793889.py:49: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta S\": female_f1 - male_f1,\n",
      "/tmp/ipykernel_15240/351793889.py:49: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta S\": female_f1 - male_f1,\n",
      "/tmp/ipykernel_15240/351793889.py:49: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta S\": female_f1 - male_f1,\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 700x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "correlation\n",
      "          Delta T  Delta G      P F1      A F1       P T       A T   Delta S\n",
      "Delta T  1.000000      NaN -0.270026  0.259476  0.384682 -0.543029 -0.403504\n",
      "Delta G       NaN      NaN       NaN       NaN       NaN       NaN       NaN\n",
      "P F1    -0.270026      NaN  1.000000  0.145667 -0.076463  0.171568  0.596925\n",
      "A F1     0.259476      NaN  0.145667  1.000000  0.172682 -0.074615 -0.706787\n",
      "P T      0.384682      NaN -0.076463  0.172682  1.000000  0.566204 -0.194710\n",
      "A T     -0.543029      NaN  0.171568 -0.074615  0.566204  1.000000  0.183189\n",
      "Delta S -0.403504      NaN  0.596925 -0.706787 -0.194710  0.183189  1.000000\n",
      "    P T stereotype        F1\n",
      "0   2.0       P F1  0.727273\n",
      "1   1.0       P F1  0.771429\n",
      "2   2.0       P F1  0.785047\n",
      "3   2.0       P F1  0.816901\n",
      "4   2.0       P F1  0.666667\n",
      "..  ...        ...       ...\n",
      "57  2.0       A F1  0.000000\n",
      "58  2.0       A F1  0.571429\n",
      "59  5.0       A F1  1.000000\n",
      "60  3.0       A F1  1.000000\n",
      "61  3.0       A F1  0.714286\n",
      "\n",
      "[62 rows x 3 columns]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1024.25x450 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "graph_5_delta_s(\"../data/he_results_mbart.txt\", tokens_dict_he,\"Hebrew\", male_stereotype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "3d70fbc7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_15240/351793889.py:43: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta S\": male_f1 - female_f1,\n",
      "/tmp/ipykernel_15240/351793889.py:43: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta S\": male_f1 - female_f1,\n",
      "/tmp/ipykernel_15240/351793889.py:43: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta S\": male_f1 - female_f1,\n",
      "/tmp/ipykernel_15240/351793889.py:43: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta S\": male_f1 - female_f1,\n",
      "/tmp/ipykernel_15240/351793889.py:43: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta S\": male_f1 - female_f1,\n",
      "/tmp/ipykernel_15240/351793889.py:43: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta S\": male_f1 - female_f1,\n",
      "/tmp/ipykernel_15240/351793889.py:43: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta S\": male_f1 - female_f1,\n",
      "/tmp/ipykernel_15240/351793889.py:43: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta S\": male_f1 - female_f1,\n",
      "/tmp/ipykernel_15240/351793889.py:43: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta S\": male_f1 - female_f1,\n",
      "/tmp/ipykernel_15240/351793889.py:43: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta S\": male_f1 - female_f1,\n",
      "/tmp/ipykernel_15240/351793889.py:43: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta S\": male_f1 - female_f1,\n",
      "/tmp/ipykernel_15240/351793889.py:43: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta S\": male_f1 - female_f1,\n",
      "/tmp/ipykernel_15240/351793889.py:43: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta S\": male_f1 - female_f1,\n",
      "/tmp/ipykernel_15240/351793889.py:43: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta S\": male_f1 - female_f1,\n",
      "/tmp/ipykernel_15240/351793889.py:49: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta S\": female_f1 - male_f1,\n",
      "/tmp/ipykernel_15240/351793889.py:43: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta S\": male_f1 - female_f1,\n",
      "/tmp/ipykernel_15240/351793889.py:43: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta S\": male_f1 - female_f1,\n",
      "/tmp/ipykernel_15240/351793889.py:49: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta S\": female_f1 - male_f1,\n",
      "/tmp/ipykernel_15240/351793889.py:49: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta S\": female_f1 - male_f1,\n",
      "/tmp/ipykernel_15240/351793889.py:49: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta S\": female_f1 - male_f1,\n",
      "/tmp/ipykernel_15240/351793889.py:49: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta S\": female_f1 - male_f1,\n",
      "/tmp/ipykernel_15240/351793889.py:43: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta S\": male_f1 - female_f1,\n",
      "/tmp/ipykernel_15240/351793889.py:49: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta S\": female_f1 - male_f1,\n",
      "/tmp/ipykernel_15240/351793889.py:49: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta S\": female_f1 - male_f1,\n",
      "/tmp/ipykernel_15240/351793889.py:49: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta S\": female_f1 - male_f1,\n",
      "/tmp/ipykernel_15240/351793889.py:49: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta S\": female_f1 - male_f1,\n",
      "/tmp/ipykernel_15240/351793889.py:43: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta S\": male_f1 - female_f1,\n",
      "/tmp/ipykernel_15240/351793889.py:43: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta S\": male_f1 - female_f1,\n",
      "/tmp/ipykernel_15240/351793889.py:49: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta S\": female_f1 - male_f1,\n",
      "/tmp/ipykernel_15240/351793889.py:49: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta S\": female_f1 - male_f1,\n",
      "/tmp/ipykernel_15240/351793889.py:49: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta S\": female_f1 - male_f1,\n",
      "/tmp/ipykernel_15240/351793889.py:49: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta S\": female_f1 - male_f1,\n",
      "/tmp/ipykernel_15240/351793889.py:49: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta S\": female_f1 - male_f1,\n",
      "/tmp/ipykernel_15240/351793889.py:49: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta S\": female_f1 - male_f1,\n",
      "/tmp/ipykernel_15240/351793889.py:49: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta S\": female_f1 - male_f1,\n",
      "/tmp/ipykernel_15240/351793889.py:49: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta S\": female_f1 - male_f1,\n",
      "/tmp/ipykernel_15240/351793889.py:49: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta S\": female_f1 - male_f1,\n",
      "/tmp/ipykernel_15240/351793889.py:49: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta S\": female_f1 - male_f1,\n",
      "/tmp/ipykernel_15240/351793889.py:49: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta S\": female_f1 - male_f1,\n",
      "/tmp/ipykernel_15240/351793889.py:49: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta S\": female_f1 - male_f1,\n",
      "/tmp/ipykernel_15240/351793889.py:49: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append({\"Delta T\": delta_t, \"Delta S\": female_f1 - male_f1,\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 700x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "correlation\n",
      "          Delta T  Delta G      P F1      A F1       P T       A T   Delta S\n",
      "Delta T  1.000000      NaN -0.012504  0.508726  0.494354 -0.469408 -0.471401\n",
      "Delta G       NaN      NaN       NaN       NaN       NaN       NaN       NaN\n",
      "P F1    -0.012504      NaN  1.000000  0.179228 -0.197691 -0.188662  0.442062\n",
      "A F1     0.508726      NaN  0.179228  1.000000 -0.038657 -0.533528 -0.803230\n",
      "P T      0.494354      NaN -0.197691 -0.038657  1.000000  0.535487 -0.084451\n",
      "A T     -0.469408      NaN -0.188662 -0.533528  0.535487  1.000000  0.372213\n",
      "Delta S -0.471401      NaN  0.442062 -0.803230 -0.084451  0.372213  1.000000\n",
      "    P T stereotype        F1\n",
      "0   1.0       P F1  0.735294\n",
      "1   1.0       P F1  0.677419\n",
      "2   1.0       P F1  0.821053\n",
      "3   1.0       P F1  0.808081\n",
      "4   1.0       P F1  0.733945\n",
      "..  ...        ...       ...\n",
      "77  2.0       A F1  0.953488\n",
      "78  3.0       A F1  0.714286\n",
      "79  4.0       A F1  0.840580\n",
      "80  3.0       A F1  0.904110\n",
      "81  2.0       A F1  0.866667\n",
      "\n",
      "[82 rows x 3 columns]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1024.25x450 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "graph_5_delta_s(\"../data/de_results_mbart.txt\", tokens_dict_de,\"German\", male_stereotype)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
